Bibliographic record
Abstract
On March 22, 2021, the US, UK, Canada and the European Union imposed a coordinated series of sanctions on current and former Chinese officials, increasing pressure on China for alleged abuses in Xinjiang. China replied with its own sanctions on European officials. Indeed, very recently after this event, on June 10, 2021, China passed a new law to ‘‘counter foreign actions’’. It has rejected the allegations of abuse, stating that the camps are ‘‘re-education’’ facilities utilised to combat terrorism. According to Xinhua Net, netizens expressed support for local brands after H&M and Nike came under fire in China for refusing to use Xinjiang cotton. In addition to this, 11 topics connected to Xinjiang cotton were on the trending list in China's Twitter-like social media platform Sina Weibo, each issue attracting tens of millions of views and discussions. The study tried to relate this phenomenon to the concept of nationalism by taking literature from nationalism and media/media censorship theories, which mainly serve as a source of legitimacy and instrumental strategy for the state. The study aims to analyse the highest government’s management of nationalism and public opinion, during the time it faces international pressure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".